Quantifying Pathogen Surveillance Using Temporal Genomic Data

ABSTRACT With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Joseph M. Chan, Raul Rabadan
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2013
Materias:	Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/0c8f70a080944f7a88199b0894086900
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:0c8f70a080944f7a88199b0894086900
record_format	dspace
spelling	oai:doaj.org-article:0c8f70a080944f7a88199b08940869002021-11-15T15:40:23ZQuantifying Pathogen Surveillance Using Temporal Genomic Data10.1128/mBio.00524-122150-7511https://doaj.org/article/0c8f70a080944f7a88199b08940869002013-03-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.00524-12https://doaj.org/toc/2150-7511ABSTRACT With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies. IMPORTANCE Surveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen’s evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose the q2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed the q2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of the q2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance.Joseph M. ChanRaul RabadanAmerican Society for MicrobiologyarticleMicrobiologyQR1-502ENmBio, Vol 4, Iss 1 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Microbiology QR1-502
spellingShingle	Microbiology QR1-502 Joseph M. Chan Raul Rabadan Quantifying Pathogen Surveillance Using Temporal Genomic Data
description	ABSTRACT With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen’s evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies. IMPORTANCE Surveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen’s evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose the q2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed the q2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of the q2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance.
format	article
author	Joseph M. Chan Raul Rabadan
author_facet	Joseph M. Chan Raul Rabadan
author_sort	Joseph M. Chan
title	Quantifying Pathogen Surveillance Using Temporal Genomic Data
title_short	Quantifying Pathogen Surveillance Using Temporal Genomic Data
title_full	Quantifying Pathogen Surveillance Using Temporal Genomic Data
title_fullStr	Quantifying Pathogen Surveillance Using Temporal Genomic Data
title_full_unstemmed	Quantifying Pathogen Surveillance Using Temporal Genomic Data
title_sort	quantifying pathogen surveillance using temporal genomic data
publisher	American Society for Microbiology
publishDate	2013
url	https://doaj.org/article/0c8f70a080944f7a88199b0894086900
work_keys_str_mv	AT josephmchan quantifyingpathogensurveillanceusingtemporalgenomicdata AT raulrabadan quantifyingpathogensurveillanceusingtemporalgenomicdata
_version_	1718427746773762048

Quantifying Pathogen Surveillance Using Temporal Genomic Data

Ejemplares similares